This document summarizes a presentation on using semi-supervised learning on Hadoop to understand user behaviors on large websites. It discusses clustering user sessions to identify different user segments, labeling the clusters, then using supervised learning to classify all sessions. Key metrics like satisfaction scores are then computed for each segment to identify opportunities to improve the user experience and business metrics. Smoothing is applied to metrics over time to avoid scaring people with daily fluctuations. The overall goal is to measure and drive user satisfaction across diverse users.
Future of AI: Blockchain & Deep LearningMelanie Swan
Future of AI: intelligence “baked in” to smart networks, blockchains to confirm authenticity and transfer value, and Deep Learning algorithms for predictive identification. This talk presents two high-impact contemporary emerging technologies: big data and deep learning algorithms, and blockchain distributed ledgers, and discusses their implications for the future of artificial intelligence.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1RJcfss.
Juan Batiz-Benet makes a short intro of IPFS (the InterPlanetary File System), a new hypermedia distribution protocol, addressed by content and identities. He also discusses the IPLD data model and example data structures (unixfs, keychain, post). Filmed at qconsf.com.
Juan Batiz-Benet is an Independent Scientist.
The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.
for more inforamation please visit our youtube channel
https://www.youtube.com/edit?o=U&video_id=EvICyArbFSs
www.rihusoft.com
Future of AI: Blockchain & Deep LearningMelanie Swan
Future of AI: intelligence “baked in” to smart networks, blockchains to confirm authenticity and transfer value, and Deep Learning algorithms for predictive identification. This talk presents two high-impact contemporary emerging technologies: big data and deep learning algorithms, and blockchain distributed ledgers, and discusses their implications for the future of artificial intelligence.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1RJcfss.
Juan Batiz-Benet makes a short intro of IPFS (the InterPlanetary File System), a new hypermedia distribution protocol, addressed by content and identities. He also discusses the IPLD data model and example data structures (unixfs, keychain, post). Filmed at qconsf.com.
Juan Batiz-Benet is an Independent Scientist.
The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.
for more inforamation please visit our youtube channel
https://www.youtube.com/edit?o=U&video_id=EvICyArbFSs
www.rihusoft.com
Announcing: Native MQTT Integration with HiveMQ and InfluxDB Cloud InfluxData
InfluxData is excited to announce the launch of a new Native MQTT connector which enables developers to configure InfluxDB Cloud to subscribe to an MQTT topic with no additional software or agents. InfluxDB Cloud will natively convert MQTT messages to Line Protocol — resulting in a faster and simplified process. Discover how to get IoT data from a HiveMQ MQTT Broker into InfluxDB with a few easy steps. Explore how to subscribe to MQTT topics and how to parse MQTT messages to determine the relevant metrics you want to ingest into InfluxDB. Learn how you can use the Native MQTT collector to ingest industrial IoT metrics quickly and start visualizing, analyzing, and transforming your data.
Join this webinar as Kudzai Manditereza and Gary Fowler discuss:
Feature deep-dive into InfluxDB's new MQTT functionality
How to configure HiveMQ and InfluxDB to quickly start ingesting data
Demo — see a live demo of the new feature and MQTT messages flowing from sensors to an HiveMQ Broker to InfluxDB Cloud
InterPlanetary File System 소개 자료입니다.
풀 한글로 작성하고 싶었으나
시간관계 상 중반부 이상은 영문 번역을 손을 못댔네요.
(이후 시간이 된다면 수정해보겠습니다.
그림 및 도표의 출처는 모두 링크로 기재되어있습니다.
본자료는 흐름을 이해하는데 사용하시고
원문 링크를 한번씩 더 읽어보시길 추천드립니다.
Nick Meyne Enterprise Architect - Capgemini
At Global Architecture Week 2015, we covered ‘Digital Currencies and Cash’ and their relevance to Tax and Welfare Authorities, concluding with the message: “It’s not about Bitcoin, it’s about the Blockchain”. Blockchain technology has the potential to enable a new mutually trusted, transparent way of sharing and transacting. In the UK Public Sector, Sir Mark Walport’s report Distributed Ledger Technology: beyond blockchain encouraged Government to assess its early use and potential. Meanwhile in the private sector, Blockchain FinTech excitement among start-ups and venture capitalists remained strong for a technology promised to be “like a whole new internet for value exchange”. But where are the real world use cases today? What is it that makes a use case more likely to succeed? In this talk, we will share and discuss a number of Capgemini examples.
Post-recording of a presentation by Kristina Hoeppner (Catalyst) at the Totara Government User Group in Wellington on 22 May 2018.
License: Creative Commons BY-SA 4.0
Live slides: https://slides.com/anitsirk/matomo-a-guide-to-your-sites-usage
Recording: https://youtu.be/yf9xCZsQBiY
Presentation from Grace Hopper Celebration 2016. Topic: Blockchain and Internet of Things (IoT) in the IBM Bluemix platform includes Demo. Speakers: Valerie Lampkin, Sumabala Nair and Carole Corley
Learn 14 Antipatterns. Three types of antipatterns. Traps that are applicable to common solutions. Development, Architecture, and Project management pattern. Feud,Golder ,
How blockchain is revolutionizing crowdfundingAhmed Banafa
According to experts, there are five key benefits of crowdfunding platforms: efficiency, reach, easier presentation, built-in PR and marketing, and near-immediate validation of concept, which explains why crowdfunding has become an extremely useful alternative to venture capital (VC), and has also allowed non-traditional projects, such as those started by in-need families or hopeful creatives, a new audience to pitch their cause.
Hyperledger Fabric Application Development 20190618Arnaud Le Hors
Slides presented at the Hyperledger Fabric Workshop in Barcelona on July 10th, 2019.
This covers the development of a Fabric application and smart contract (i.e. chaincode), with some tips on good practices and the IBM Blockchain Platform extension for VS Code.
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
A blockchain, originally block chain, is a growing list of records, called blocks, that are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. But Blockchain is not simply a mere technology that may fade away rather it is a concept that serves a wide variety of purpose and is one of the most trusted emerging technology of the era. This is a small attempt at how Blockchain technology may revolutionize the Cloud platforms.
Project Link : https://github.com/vedantmane/images
Taboola's experience with Apache Spark (presentation @ Reversim 2014)tsliwowicz
At taboola we are getting a constant feed of data (many billions of user events a day) and are using Apache Spark together with Cassandra for both real time data stream processing as well as offline data processing. We'd like to share our experience with these cutting edge technologies.
Apache Spark is an open source project - Hadoop-compatible computing engine that makes big data analysis drastically faster, through in-memory computing, and simpler to write, through easy APIs in Java, Scala and Python. This project was born as part of a PHD work in UC Berkley's AMPLab (part of the BDAS - pronounced "Bad Ass") and turned into an incubating Apache project with more active contributors than Hadoop. Surprisingly, Yahoo! are one of the biggest contributors to the project and already have large production clusters of Spark on YARN.
Spark can run either standalone cluster, or using either Apache mesos and ZooKeeper or YARN and can run side by side with Hadoop/Hive on the same data.
One of the biggest benefits of Spark is that the API is very simple and the same analytics code can be used for both streaming data and offline data processing.
The millions of people that use Spotify each day generate a lot of data, roughly a few terabytes per day. What does it take to handle datasets of that scale, and what can be done with it? I will briefly cover how Spotify uses data to provide a better music listening experience, and to strengthen their busineess. Most of the talk will be spent on our data processing architecture, and how we leverage state of the art data processing and storage tools, such as Hadoop, Cassandra, Kafka, Storm, Hive, and Crunch. Last, I'll present observations and thoughts on innovation in the data processing aka Big Data field.
Announcing: Native MQTT Integration with HiveMQ and InfluxDB Cloud InfluxData
InfluxData is excited to announce the launch of a new Native MQTT connector which enables developers to configure InfluxDB Cloud to subscribe to an MQTT topic with no additional software or agents. InfluxDB Cloud will natively convert MQTT messages to Line Protocol — resulting in a faster and simplified process. Discover how to get IoT data from a HiveMQ MQTT Broker into InfluxDB with a few easy steps. Explore how to subscribe to MQTT topics and how to parse MQTT messages to determine the relevant metrics you want to ingest into InfluxDB. Learn how you can use the Native MQTT collector to ingest industrial IoT metrics quickly and start visualizing, analyzing, and transforming your data.
Join this webinar as Kudzai Manditereza and Gary Fowler discuss:
Feature deep-dive into InfluxDB's new MQTT functionality
How to configure HiveMQ and InfluxDB to quickly start ingesting data
Demo — see a live demo of the new feature and MQTT messages flowing from sensors to an HiveMQ Broker to InfluxDB Cloud
InterPlanetary File System 소개 자료입니다.
풀 한글로 작성하고 싶었으나
시간관계 상 중반부 이상은 영문 번역을 손을 못댔네요.
(이후 시간이 된다면 수정해보겠습니다.
그림 및 도표의 출처는 모두 링크로 기재되어있습니다.
본자료는 흐름을 이해하는데 사용하시고
원문 링크를 한번씩 더 읽어보시길 추천드립니다.
Nick Meyne Enterprise Architect - Capgemini
At Global Architecture Week 2015, we covered ‘Digital Currencies and Cash’ and their relevance to Tax and Welfare Authorities, concluding with the message: “It’s not about Bitcoin, it’s about the Blockchain”. Blockchain technology has the potential to enable a new mutually trusted, transparent way of sharing and transacting. In the UK Public Sector, Sir Mark Walport’s report Distributed Ledger Technology: beyond blockchain encouraged Government to assess its early use and potential. Meanwhile in the private sector, Blockchain FinTech excitement among start-ups and venture capitalists remained strong for a technology promised to be “like a whole new internet for value exchange”. But where are the real world use cases today? What is it that makes a use case more likely to succeed? In this talk, we will share and discuss a number of Capgemini examples.
Post-recording of a presentation by Kristina Hoeppner (Catalyst) at the Totara Government User Group in Wellington on 22 May 2018.
License: Creative Commons BY-SA 4.0
Live slides: https://slides.com/anitsirk/matomo-a-guide-to-your-sites-usage
Recording: https://youtu.be/yf9xCZsQBiY
Presentation from Grace Hopper Celebration 2016. Topic: Blockchain and Internet of Things (IoT) in the IBM Bluemix platform includes Demo. Speakers: Valerie Lampkin, Sumabala Nair and Carole Corley
Learn 14 Antipatterns. Three types of antipatterns. Traps that are applicable to common solutions. Development, Architecture, and Project management pattern. Feud,Golder ,
How blockchain is revolutionizing crowdfundingAhmed Banafa
According to experts, there are five key benefits of crowdfunding platforms: efficiency, reach, easier presentation, built-in PR and marketing, and near-immediate validation of concept, which explains why crowdfunding has become an extremely useful alternative to venture capital (VC), and has also allowed non-traditional projects, such as those started by in-need families or hopeful creatives, a new audience to pitch their cause.
Hyperledger Fabric Application Development 20190618Arnaud Le Hors
Slides presented at the Hyperledger Fabric Workshop in Barcelona on July 10th, 2019.
This covers the development of a Fabric application and smart contract (i.e. chaincode), with some tips on good practices and the IBM Blockchain Platform extension for VS Code.
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
A blockchain, originally block chain, is a growing list of records, called blocks, that are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. But Blockchain is not simply a mere technology that may fade away rather it is a concept that serves a wide variety of purpose and is one of the most trusted emerging technology of the era. This is a small attempt at how Blockchain technology may revolutionize the Cloud platforms.
Project Link : https://github.com/vedantmane/images
Taboola's experience with Apache Spark (presentation @ Reversim 2014)tsliwowicz
At taboola we are getting a constant feed of data (many billions of user events a day) and are using Apache Spark together with Cassandra for both real time data stream processing as well as offline data processing. We'd like to share our experience with these cutting edge technologies.
Apache Spark is an open source project - Hadoop-compatible computing engine that makes big data analysis drastically faster, through in-memory computing, and simpler to write, through easy APIs in Java, Scala and Python. This project was born as part of a PHD work in UC Berkley's AMPLab (part of the BDAS - pronounced "Bad Ass") and turned into an incubating Apache project with more active contributors than Hadoop. Surprisingly, Yahoo! are one of the biggest contributors to the project and already have large production clusters of Spark on YARN.
Spark can run either standalone cluster, or using either Apache mesos and ZooKeeper or YARN and can run side by side with Hadoop/Hive on the same data.
One of the biggest benefits of Spark is that the API is very simple and the same analytics code can be used for both streaming data and offline data processing.
The millions of people that use Spotify each day generate a lot of data, roughly a few terabytes per day. What does it take to handle datasets of that scale, and what can be done with it? I will briefly cover how Spotify uses data to provide a better music listening experience, and to strengthen their busineess. Most of the talk will be spent on our data processing architecture, and how we leverage state of the art data processing and storage tools, such as Hadoop, Cassandra, Kafka, Storm, Hive, and Crunch. Last, I'll present observations and thoughts on innovation in the data processing aka Big Data field.
A talk I gave on what Hadoop does for the data scientist. I talk about data exploration, NLP, Classifiers, and recommendation systems, plus some other things. I tried to depict a realistic view of Hadoop here.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
This is a talk I gave at Data Science MD meetup. It was based on the talk I gave about a month before at Data Science NYC (http://www.slideshare.net/DonaldMiner/data-scienceandhadoop). I talk about data exploration, NLP, Classifiers, and recommendation systems, plus some other things. I tried to depict a realistic view of Hadoop here.
Bitkom Cray presentation - on HPC affecting big data analytics in FSPhilip Filleul
High value analytics in FS are being enabled by Graph, machine learning and Spark technologies. To make these real at production scale HPC technologies are more appropriate than commodity clusters.
Explore big data at speed of thought with Spark 2.0 and SnappydataData Con LA
Abstract:
Data exploration often requires running aggregation/slice-dice queries on data sourced from disparate sources. You may want to identify distribution patterns, outliers, etc and aid the feature selection process as you train your predictive models. As you begin to understand your data, you want to ask ad-hoc questions expressed through your visualization tool (which typically translates to SQL queries), study the results and iteratively explore the data set through more queries. Unfortunately, even when data sets can be in-memory, large data set computations take time breaking the train of thought and increasing time to insight . We know Spark can be fast through its in-memory parallel processing. But, Spark 1.x isn’t quite there. Spark 2.0 promises to offer 10X better speed than its predecessor. Spark 2.0 ushers some impressive improvements to interactive query performance. We first explore these advances - compiling the query plan eliminating virtual function calls, and other improvements in the Catalyst engine. We compare the performance to other popular popular query processing engines by studying the spark query plans. We then go through SnappyData (an open source project that integrates Spark with a database that offers OLTP, OLAP and stream processing in a single cluster) where we use smarter data colocation and Synopses data (.e.g. Stratified sampling) to dramatically cut down on the memory requirements as well as the query latency. We explain the key concepts in summarizing data using structures like stratified sampling by walking through some examples in Apache Zeppelin notebooks (a open source visualization tool for spark) and demonstrate how we can explore massive data sets with just your laptop resources while achieving remarkable speeds.
Bio:
Jags is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory Bio:
Jags Ramnarayan is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory products.
Sumo Logic QuickStart Webinar - Jan 2016Sumo Logic
QuickStart your Sumo Logic service with this exclusive webinar. At these monthly live events you will learn how to capitalize on critical capabilities that can amplify your log analytics and monitoring experience while providing you with meaningful business and IT insights
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
Topics covered include:
- Scaling Vertically
- Hardware Considerations
- Index Optimization
- Schema Design
- Sharding
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Measuring CDN performance and why you're doing it wrongFastly
Integrating content delivery networks into your application infrastructure can offer many benefits, including major performance improvements for your applications. So understanding how CDNs perform — especially for your specific use cases — is vital. However, testing for measurement is complicated and nuanced, and results in metric overload and confusion. It's becoming increasingly important to understand measurement techniques, what they're telling you, and how to apply them to your actual content.
In this session, we'll examine the challenges around measuring CDN performance and focus on the different methods for measurement. We'll discuss what to measure, important metrics to focus on, and different ways that numbers may mislead you.
More specifically, we'll cover:
Different techniques for measuring CDN performance
Differentiating between network footprint and object delivery performance
Choosing the right content to test
Core metrics to focus on and how each impacts real traffic
Understanding cache hit ratio, why it can be misleading, and how to measure for it
Jethro data meetup index base sql on hadoop - oct-2014Eli Singer
JethroData Index based SQL on Hadoop engine.
Architecture comparison of MPP / Full-Scan sql engines such as Impala and Hive to index-based access such as Jethro.
SQL and NoSQL NYC meetup Oct 20 2014
Boaz Raufman
Similar to Dataiku hadoop summit - semi-supervised learning with hadoop for understanding user web behaviours (20)
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Dataiku
In our 3rd applied machine learning online course, we'll dive into different methods for data preparation, including handling missing values, dummification and rescaling.
Applied Data Science Course Part 2: the data science workflow and basic model...Dataiku
In the second part of our applied machine learning online course, you'll get an overview of the different steps in the data science workflow as well as a deep dive in 3 basic types of models: linear, tree-based and clustering.
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.
The Rise of the DataOps - Dataiku - J On the Beach 2016 Dataiku
Many organisations are creating groups dedicated to data. These groups have many names : Data Team, Data Labs, Analytics Teams….
But whatever the name, the success of those teams depends a lot on the quality of the data infrastructure and their ability to actually deploy data science applications in production.
In that regards a new role of “DataOps” is emerging. Similar, to Dev Ops for (Web) Dev, the Data Ops is a merge between a data engineer and a platform administrator. Well versed in cluster administration and optimisation, a data ops would have also a perspective on the quality of data quality and the relevance of predictive models.
Do you want to be a Data Ops ? We’ll discuss its role and challenges during this talk
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem.
As Data Manager, you know the challenges ahead:
- Multitudes of technology choices to make
- Building a team and solving the skill-set disconnect
- Data can be deceiving...
- Figuring out what the successful data product must be
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Before Kaggle : from a business goal to a Machine Learning problem Dataiku
Many think that a Data Science is like a Kaggle competition. There are, however big differences in the approach. This presentation is about designing carefully your evaluation scheme to avoid overfitting and unexpected production performances.
This is a presentation by Pierre Gutierrez (Dataiku’s data scientist).
Retrouvez l'intégralité de la présentation commune de Dataiku et Coyote sur la "Valorisation des données".
Cette présentation a été réalisée dans le cadre du Symposium du 04 Juin 2015, organisé par le Club Urba-EA et le Club Pilotes de Processus.
Plus d'informations sur www.dataiku.com
Dataiku productive application to production - pap is may 2015 Dataiku
Beyond Predictive Analytics : Deploying apps to production and keep them improving
Some smart companies have been putting predictive application in production for decades. Still, either because of lack of sharing or lack of generality, there is still no single and obvious way to put a predictive application in production today.
As a consequence, for most companies, transitioning analytics from development to production is still “the next frontier”.
Behind the single word "production” lays a great number of questions like: what exactly do you put in production: data, model, code all three ? Who is responsible for maintenance and quality check over time : business, tech or both ? How can I make my predictive app continuously improve and check that it delivers the promised business value over time ? What are the best practice for maintenance and updates by the way ? Will my data scientists keep working after first development or should I lay half of them off ? etc…
Let’s make a small analogy with the development of web sites in the 90’s and early 00’s :
Back then, the winners where not necessarily the web sites with an amazing design, but a winner had clearly made the necessary efforts and had a robust way to put their web site reliabily in production
Today, every web developper can enjoy the confort of Heroku, Amazon, Github, docker, Angular, bootstrap … and so we forget. How much time before we get the same confort for the predictive world ?
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku
Between traditional Business Intelligence and "Big Data" approaches, many companies need to innovate and work in a hybrid manner. How and with what tools can business and technical profiles collaborate productively together? lorian Douetteau, Dataiku's CEO, answers these questions.
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku
This is a presentation made on the 13th August 2014 at the SF Data Mining Meetup at Trulia. It's about Dataiku and the Kaggle Personalized Web Search Ranking challenge sponsored by Yandex
Dataiku big data paris - the rise of the hadoop ecosystemDataiku
Snapshot of the hadoop ecosystem at the beginning of 2014, with the rise of real time and in memory processing distributed frameworks that complement and supplant the Map Reduce paradigm
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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• Communication Mining Overview
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Bob Boule
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Gopinath Rebala
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
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Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
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https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
4. Motivation
• CxO
– Pages Views, Unique Visitors, Dollars, Subscription
• Editor / Product Manager
– Time Spent, Comments
• Users
– Content
What does matter on a web site ?
5. Key Usage Metrics
• Publisher
– Time Spent on Page
– Number of pages seen
– Number of comments
– Move to Subscription
• Search Engine
– Click on first hits / re-click
– Rephrasing ratio
– Will come back tomorrow
– Click on Advertisting
• Online Game
– Time spent in the game
– Level Progress
– In-App Purchase
6. The Quest for the Missing Proxy
• Publisher
– Time Spent on Page
– Number of pages seen
– Number of comments
– User Satisfaction
– Move to Subscription
• Search Engine
– Click on first hits / re-click
– Rephrasing ratio
– User Satisfaction
– Will come back tomorrow
– Click on Advertisting
• Online Game
– Time spent in the game
– Level Progress
– User Satisfaction
– In-App Purchase
U
S
E
R
7. Question
How to measure and drive user satisfaction on a
large web sites with very diverse usage patterns
?
8. The Problem
New Comers From
Google News
People Coming
from twitter and
Facebook Posts
People coming to
the website almost
each and everyday
People that loves
to comment
Foreigners Robots
People fond of
sport section only
…. …..
BEHAVIOUR DIVERSITY
THE AVERAGED
METRICS WOULD
HIDE
IMPORTANT
VARIATION ON
SPECIFIC SEGMENTS
9. SubProblem 1: Hard Segments
• Segments Users per
Number of visits per
month
– > 20 days per month
-> Engaged Users
• Segment per
transformed or not
• Segment per country
10. Subproblem 2: Hard Metrics
• Newspaper
Time Spent on the website
log(Number of page
views) + Number of actions
• Search engine
Click Ratio
Click ratio
• E-Commerce
Transformation Ratio
12. Semi-Supervised Learning
All Labeled Data
All Unlabeled Data
Some Labeled Data
Lots of Unlabeled
Data
Training Data
Supervised
Learning
Unsupervised
Learning
Semi-
Supervised
Learning
Model
Model
Model
13. ½ SL – Natural Language Processing
I hope I’ll enjoy Amsterdam, and not only because of Hadoop
Je pense bien passer du bon temps à Amsterdam, et pas seulement grâce à Hadoop
Statistical Knowledge
Text Structure
(Unsupervised)
Aligned Corpus
(Supervised)
14. ½ SL Applied to Web Sessions
Lots of customer sessions
Not so many concrete customer
feedbacks
Subscription
15. Semi-Supervised Learning
3 Approaches
• Generative Models, e.g. gaussian fits
– All Data fits a gaussian distribution with parameter X
– Find X that better fit distribution of both labeled data and
unlabeled data
• Fits with costs
– Supervised learning with a costs function that capture a
distance between point related to the unlabeled data
structure
• Ad-hoc : Combine unsupervised, then supervised
19. Our Approach
1. (Lots of ) Data preparation to build miningful
user session
2. Clustering sessions and validate/tag those
clusters by end users
3. Create Predictive User Satisfaction Metrics
4. Follow those metrics !
20. Data Prep: Overview
Step 1
Build Sessions
Pig
Step 2
Parse IP/Time/..
Custom Python
(or )
Step 3
Parse Sequences
Hive or Python
custom
Step 4
Build user-level
stats
Hive
RAW DATA
READY FOR ML
21. Step 1. Build Session
• Use Hive ( Or Pig)
• Group into “Session”
• Depending on the variable
– IP, Device Select only one per log
– URL, Event Create an ordered array that
represents the sequence of events in the session
22. Step 2 : Basic Feature
• IP Address Location, City
• User-Agent Device
• Timestamp User Time Day or night ?
Python + Hadoop Streaming
Option 1 Option 2
24. Step 3: Session Signals
• Simple Signals
– Number of Page Views
– Time Spent …..
– Etc…
• Limitation
It might not help that much to differentiate
behaviour
25. More Elaborate: N-Grams Model
Field Unit Sample 1-Gram 2-Gram 3-Gram
Protein Amino
Acid
Cys-Gly-Leu Cys, Gly, Leu Cys-Gly, Gly-Leu Cys-Gly-Leu
DNA Base Pair …ATTAGCAT.. A,T,T,A AT,TT,TA,AG, ATT,TTA,TAG,..
NLP (word) Character ..some like it hot… s,o,m,e,l,i,t.. so,om,el,li,it som,ome,me_,_li,lik,..
NLP
(character)
Word ..some like it hot… some,like,it some-like,like-it some-like-it,
like-it-hote
26. N-Grams Model For Sessions
Field Unit Sample 1-Gram 2-Gram 3-Gram
Protein Amino
Acid
Cys-Gly-Leu Cys, Gly, Leu Cys-Gly, Gly-Leu Cys-Gly-Leu
DNA Base Pair …ATTAGCAT.. A,T,T,A AT,TT,TA,AG, ATT,TTA,TAG,..
NLP (word) Character ..some like it hot… s,o,m,e,l,i,t.. so,om,el,li,it som,ome,me_,_li,lik,..
NLP
(character)
Word ..some like it hot… some,like,it some-like,like-it some-like-it,
like-it-hote
Web Sessions Page View [/home , /products, /trynow,
/blog]
/home, /products, /trynow,
/blog
/home /products, /products
/trynow, /trynow /blog
/home-/products-/trynow,
/products-/trynow-/blog
27. Session N-Grams Analytics
Campaign / URL / Event Detailed Token Simple Token
utm=google_search google-search-my-site google-search
/home home home
/search?q=baseball search-baseball search
click=www.nfl.com click-nfl click
/sport/new-player-com.. sport/new-player-comming sport
/search?q=Mick+JONES search-mick+jones search
click=www.nfl.com click-nfl click
/sport/new-player-com.. sport/new-player/comming sport
/politics/home politics-home politics
Important Tricks:
• Incorporate the first referrer / marketing campaign as FIRST TOKEN
• Build two level of tokens: detailed, and category only
N-Grams Fine Grain N-Grams Coarse Grain
28. How To In Practice
• Hive query using the n-grams UDF
• Compute the LLR (Least-Likehood Ratio) Metrics
• Keep the most frequent n-grams of each type (detailed
/ non detailed) as features for the session
• Hint : Set the frequency limit so that > 90% session
can be described by a non-detailed n-gram
29. Step 4. Cohort-like data
• Per cookie compute metrics
– Nb. Days since first visit
– Nb visits in the last 30 days
– Average session time
– …
• Reintegrate this information
• Easily achieved with a HiveQL query
30. Machine Learning for HDFS Data
Kind Algorithms
for clustering
Simplicity TRAIN set size
Apache Mahout MapReduce ~ 10 available Expert TERABYTES
Python
(Scikit+Pandas+…
)
Out for training /
In for apply
~ 20 available
(including bi-
clustering)
Medium (10GB)
1 SERVER RAM
H2O Separate Cluster 1 (kMeans) Medium (100GB – 1TB)
CLUSTER RAM
Open Source R +
Hadoop
Varies Varies Varies Varies
Open Source R +
Pattern
(Casacding)
Out for training
/ In for apply
> 3 Medium (1GB)
1 Server RAM in
R
Spark + MLLib Separate Cluster 1 Medium (100GB – 1TB)
CLUSTER RAM
31. How Big is out data here ?
Step 1
Build Sessions
Step 2
Parse IP/Time/..
Step 3
Parse Sequences
Step 4
Build user-level
stats
RAW DATA
READY FOR ML
Uncompressed data size, for 1 year worth of log on a website with
10 Millions Unique Visitors per month
10 GB5TB
32. Clustering With Scikit on HDFS
1. Use Pydoop to get data on train server
2. Use pandas to read data transform to numerical
3. Kmeans().fit()
4. Ipython to draw some graphs
5. Enjoy
or
35. Clustering & Cluster Sampling
Take a balanced number of samples
in each cluster, close to the centroid
36. Labelling
0’ 00
0’ 12
1’ 04
1’ 45
3’ 02
Visualizing Sessions
Search for a
specific Topic
Labelling
I can guess what this guy was
doing !!!
37. Labelling
Search for a
specific Topic
Newcomer
from Google
News
Foreigner
Discovering The
Site
Fan that loves
to comment
Home Page
Wanderer
Dark Bot
(Competitor?)
38. What if ?
Search for a
specific Topic
Newcomer
from Google
News
Foreigner
Discovering The
Site
Fan that loves
to comment
Home Page
Wanderer
Dark Bot
(Competitor?)
39. Supervised Learning
Search for a
specific Topic
Newcomer
from Google
News
Foreigner
Discovering The
Site
Fan that loves
to comment
Home Page
Wanderer
Dark Bot
(Competitor?)
Independently from the clusters, used the
trained examples in order to classify each
session in the predefined segments
40. Supervised Learning : e.g. in python
• Load the data and the label in
python (Pandas)
• Fit the labeled sessions against
a model
• Save the model in HDFS
(python pickle)
• Run the model against all the
data (Hadoop Streaming)
We’ve got a tool to help you
do that in Data Science Studio
He’s called the Doctor and he’s
fun to use !
41. Compute Metrics Per Segments
Search for a
specific Topic
Newcomer
from Google
News
Foreigner
Discovering The
Site
Fan that loves
to comment
Home Page
Wanderer
Dark Bot
(Competitor?)
0.3€ per session
0.23€ acquisition costs
``
`
13k sessions
1.3€ per session
0.23€ acquisition costs
938k sessions
938k sessions
0.3€ per session
0.23€ acquisition costs
738k sessions
0.83€ per session
0.73€ acquisition costs
68k sessions
0.3€ per session
1.23€ acquisition costs
1k sessions
0€ per session
0€ acquisition costs
42. User Satisfaction Metrics
• Future-Based Metrics
– Will the user most
likely subscribe/pay in
the future ?
• Expressed-Opinion
– Does he like satisfied
from its behaviour ?
43. Opinion-Based Training For User Satisfaction
User Feedbacks as “Labels” to build a model
on satisfaction
“Predict” a satisfaction score
for non-trained session
Session Data
Feedbacks
Scored
Session
HYPOTHESIS : IF TWO USERS HAVE SIMILAR NAVIGATION PATTERNS
THEY HAVE SIMILAR USER SATISFACTION LEVELS
(100 Million Sessions)
(10.000 feedbacks)
44. Compute Metrics Per Segments
Search for a
specific Topic
Newcomer
from Google
News
Foreigner
Discovering The
Site
Fan that loves
to comment
Home Page
Wanderer
Dark Bot
(Competitor?)
0.3€ per session
0.23€ acquisition costs
``
`
13k sessions
1.3€ per session
0.23€ acquisition costs
938k sessions
938k sessions
0.3€ per session
0.23€ acquisition costs
738k sessions
0.83€ per session
0.73€ acquisition costs
68k sessions
0.3€ per session
1.23€ acquisition costs
1k sessions
0€ per session
0€ acquisition costs
SATISFACTION SCORE 0.87§
SATISFACTION SCORE 0.37
SATISFACTION SCORE 0.28
SATISFACTION SCORE 0.12
SATISFACTION SCORE 0.28 SATISFACTION SCORE 0.12
45. Data in Time: Smoothing
In Red : The Base Metric
In Blue : The smoothed metricRAW DATA MAY VARY A LOT
FROM DAYS TO DAYS
IT WILL SCARE PEOPLE
46. Exponential Smoothing In Hive
SELECT segment
moving_avg(day, satisfaction, 15, 1.52, 15, DATEDIFF(‘2014-15-01’, ‘2014-01-01’))
FROM
stats
GROUP BY segment
These factors determine
whether your smooth a lot
or not, and over how many days
47. Final : Follow Smoothed Satisfaction
Search for a
specific Topic
Newcomer
from Google
News
Foreigner
Discovering The
Site
Fan that loves
to comment
Home Page
Wanderer
Dark Bot
(Competitor?)
Follow Statisfaction Metric Per Segment
Damn
our latest
release
has diverging
effects
on segments
48. Thank You !
Florian Douetteau
@fdouetteau
Questions now or later:
florian.douetteau@dataiku.com
dataiku.com